Furthermore, precipitation nowcasting is a more challenging than classical spatio-temporal prediction tasks due to the complexity and the chaotic nature of the atmospheric environment. In fact, clouds have variable speed, and they may accumulate, dissipate or change rapidly [1]}. Indeed, precipitation maps based on radar images are obtained with a lower frequency (1 frame every 5-15 minutes) than video images (25-30 frames every second). For instance, some computer vision methods are based on optical flow [2]} which estimates object movement in a sequence of images. However, optical flow is unable to represent the sudden change in weather since it makes assumptions that are clearly violated, e.g., the amount of rain will not change over time.
